A Two-Step Optimised BERT-Based NLP Algorithm for Extracting Sentiment from Financial News

Sentiment analysis involving the identification of sentiment polarities from textual data is a very popular area of research. Many research works that have explored and extracted sentiments from textual data such as financial news have been able to do so by employing Bidirectional Encoder Representations from Transformers (BERT) based algorithms in applications with high computational needs, and also by manually labelling sample data with help from financial experts. We propose an approach which makes possible the development of quality Natural Language Processing (NLP) models without the need for high computing power, or for inputs from financial experts on labelling focused dataset for NLP model development. Our approach introduces a two-step optimised BERT-based NLP model for extracting sentiments from financial news. Our work shows that with little effort that involves manually labelling a small but relevant and focused sample data of financial news, one could achieve a high performing and accurate multi-class NLP model on financial news.